Stacking Ensemble Approach for Enhanced Heart Disease Prediction: A Comparative Analysis of Advanced Machine Learning Models

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 1

Year of Publication : 2026

Authors : Ahmed Qtaishata, Wan Suryani Wan Awangb

10.14445/23488549/IJECE-V13I1P112

How to Cite?

Ahmed Qtaishata, Wan Suryani Wan Awangb, "Stacking Ensemble Approach for Enhanced Heart Disease Prediction: A Comparative Analysis of Advanced Machine Learning Models," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 1, pp. 135-147, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I1P112

Abstract:

The leading cause of death globally is Cardiovascular Disease (CVD), and the number of deaths due to CVD is more than 17.9 million people annually, based on statistics published by the World Health Organization (WHO). To know how to help patients better, an early and accurate prediction of heart disease is necessary to provide early clinical intervention. This paper analyzes how improved risk forecasting of heart disease can be performed using advanced algorithms of Machine Learning (ML) using a benchmark clinical dataset. Our models were evaluated and systematically deployed five models, Extreme Gradient Boosting (XGBoost), LightGBM, CatBoost, Elastic Net, and a Stacking Classifier in a single predictive pipeline. The Pipeline consisted of preprocessing data, selecting features with Recursive Feature Elimination (RFE), tuning hyperparameters with RandomizedSearchCV, and evaluating the strict models with such metrics as Accuracy, Precision, Recall, F1-score, etc. The results of the experiment suggest that all the models performed very well, with the ensemble-based models performing better than the individual models. The Stacking Classifier performed the most generalized results with a Test Accuracy of 87.70, F1-score of 0.88, as well as a Recall of 91% on the heart disease cases. CatBoost and LightGBM could also perform competently with the test accuracy of 85.25% and 83.60% respectively. The state-of-the-art methods are compared with the proposed models experimentally and demonstrate that the latter is more accurate and robust. The outcomes give support to the fact that ensemble and hybrid ML approaches have the potential to enhance clinical decision support to predict heart disease risks. Further research will involve the use of Explainable Artificial Intelligence (XAI), expansion of datasets in size and heterogeneity, and prospective validation in medical practice.

Keywords:

CatBoost, ElasticNet, Heart Disease Prediction, LightGBM, Machine Learning, Stacking Classifier, XGBoost.

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